(11b) Optimal Design of Large-Scale Carbon Capture, Utilization, and Storage (CCUS) Networks Under Uncertainty

Carbon Capture, Utilization, and Storage (CCUS) is an enabling pathway for industrial decarbonization^[1]. As CCUS involves large number of CO₂ sources and sinks, and their potential connectivity, designing optimal CCUS supply chain networks is non-trivial. In recent years, several mathematical models have been developed for CCUS network optimization^[2,3]. The scale and complexity of these models vary significantly, as each focuses on different aspects of CCUS network design. Although underground reservoirs are available for CO₂ sequestration, uncertainties in the exact storage volumes and the future market prices of captured CO₂ pose significant additional challenges. Deterministic methods would fail to design even feasible, let alone optimal, CCUS networks under uncertain storage volumes and future selling prices of CO₂.

In this work^[4], we employ a two-stage stochastic optimization approach^[5,6] to design not only feasible but also most economic CCUS networks under a wide range of future scenarios of geological storage volumes and selling prices. Considering 335 sinks and their known continuous distribution functions of their volumes, we essentially have an infinite number of scenario realizations. For these infinite realizations of future outcomes, we use the Sample Average Approximation (SAA) method^[7], which is a Monte-Carlo simulation-based approach to generate a reduced scenario set to create an approximated problem to the original. We generate multiple approximated problems and solve them to obtain different candidate CCUS network designs. We find the best design by choosing the one which fits best in all scenarios in terms of the total cost, which includes the carbon tax resulting from undesirable deviations from the capture requirements considered across all batches. SAA allows us to obtain guaranteed statistical lower and upper bounds on the true optimal CCUS network design cost.

We apply the method for the design of potential nationwide and regional CCUS networks in the United States based on emission data from the GHGRP^[8] database and the underground storage estimates as reported by the USGS^[9,10]. Our analysis suggests that uncertain geological storage volumes have a significant effect on the topology and the feasibility of large networks, underscoring the need for careful consideration of these factors in CO₂ storage selection. Interestingly, by explicitly incorporating storage uncertainties into the network design, we reduce expected deviations from capture targets by over 96%, with only a marginal (≤ 4%) increase in overall CCUS costs. In contrast, uncertainty in CO₂ selling prices has minimal impact on the feasibility and topology of the CCUS network. Regional CCUS costs differ significantly, influenced by sequestration availability, utilization demand, and proximity to sources. While this study primarily focuses on the United States, our CCUS network design and optimization framework is broadly applicable to any region with data on CO₂ emission sources and underground storage estimates. This methodology provides a powerful tool for identifying optimal locations for CCUS deployment, supporting policymakers in making informed decisions on financial incentives, and directing investments to high-impact regions. By integrating uncertainty into CCUS infrastructure planning, this approach de-risks large-scale investments and enhances the viability of CCUS as a critical tool for achieving global decarbonization goals.

Keywords: CCUS Network Design, Decarbonization, Geological Storage Uncertainty, Stochastic Optimization, Sample Average Approximation

References:

[1] Hasan, M. M. F., Zantye, M. S., Kazi, M.K. "Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective." Computers & Chemical Engineering 166 (2022): 107925.

[2] Hasan, M. M. F., Boukouvala, F., First, E. L., Floudas, C. A. Nationwide, Regional and Statewide CO2 Capture, Utilization and Sequestration Supply Chain Network Optimization. Industrial & Engineering Chemistry Research, 2014, 53(18), 7489–7506.

[3] Zhang, X., Li, K., Wei, N., Li, Z. and Fan, J.L., 2022. Advances, challenges, and perspectives for CCUS source-sink matching models under carbon neutrality target. Carbon Neutrality, 1(1), p.12.

[4] Aras, C. M., Ntaimo, L., Hasan, M. M. F. Optimal Design of Carbon Capture, Utilization and Storage Networks Under Uncertain Geological Storage Volumes and CO₂ Price. Under Review in Computers and Chemical Engineering

[5] Ntaimo, L. Computational Stochastic Programming: Models, Algorithms, and Implementation; Springer Cham, 2024.

[6] Li, C.; Grossmann, I. E. A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty. Frontiers in Chemical Engineering 2020, 2.

[7] Kleywegt, A. J.; Shapiro, A.; Homem-De-Mello, T. The Sample Average Approximation Method For Stochastic Discrete Optimization. Society for Industrial and Applied Mathematics 2001, 12, 479–502.

[8] U.S. Environmental Protection Agency Office of Atmospheric Protection Greenhouse Gas Reporting Program (GHGRP). 2024; www.epa.gov/ghgreporting, Accessed: June 2024.

[9] Warwick, P. D. et al. National Assessment of Geologic Carbon Dioxide Storage Resources-Results. 2013.

[10] Energy Resources Program National Assessment of Carbon Dioxide Enhanced Oil Recovery and Associated Carbon Dioxide Retention Resources-Results. 2019.